Multiuser scheduling for mimo broadcast channels with finite rate feedback

ABSTRACT

System and methodologies are provided herein for multiuser scheduling in a multiple-input multiple-output (MIMO) communication system. Various aspects described herein facilitate full feedback scheduling, wherein multiuser scheduling is performed based on an antenna selection and signal quality feedback, such as signal-to-interference-plus-noise ratio (SINR) feedback, from respective users. Based on information received from respective users, independent information streams can be transmitted from respective transmit antennas to respective users with the highest signal quality. Receive antenna selection can also be employed to allow respective users to select a single receive antenna on which information is to be received. Additional aspects described herein facilitate quantized feedback scheduling, wherein scheduling is performed based on signal quality feedback that is quantized into a finite number of bits by respective users.

CROSS-REFERENCE

This application is a continuation of U.S. patent application Ser. No.12/108,485, filed on Apr. 23, 2008, entitled “MULTIUSER SCHEDULING FORMIMO BROADCAST CHANNELS WITH FINITE RATE FEEDBACK”, which claims thebenefit of U.S. Provisional Patent Application Ser. No. 60/914,810,filed on Apr. 30, 2007, entitled “MULTIUSER SCHEDULING FOR MIMOBROADCAST CHANNELS WITH FINITE RATE FEEDBACK”, the entireties of whichare herein incorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to wireless communicationssystems, and more particularly to techniques for multi-user schedulingin a wireless communication system.

BACKGROUND

Multiuser diversity is a form of selection diversity among users in awireless communication system that arises from independent fadingchannels between a base station and multiple users. A variety ofmultiuser scheduling algorithms have been proposed that take advantageof multiuser diversity for downlink transmissions in multiple-inputmultiple-output (MIMO) systems. Most of these existing approaches arebased on time division multiple access (TDMA). However, due at least inpart to the fact that a base station using TDMA can transmit to only oneuser at a time, the maximum sum-rate achievable by such approaches forMIMO broadcast channels is only a small fraction of the total sum-ratecapacity of MIMO broadcast channels.

Other existing scheduling algorithms, such as dirty paper coding (DPC),can achieve MIMO broadcast channel capacity by serving multiple userssimultaneously. However, achieving an optimum transmission policy usinga scheduling algorithm such as DPC is computationally complex. Further,while low-complexity DPC algorithms have been proposed, such algorithmsrequire perfect knowledge of channel state information (CSI) at thetransmitter. However, perfect knowledge of CSI at the transmitter isgenerally impossible to obtain in practice due to system limitations.Instead, a limited CSI feedback load is generally provided to a basestation from users at a finite rate. As the available feedback load of asystem decreases, traditional multiuser scheduling algorithms experiencea significant reduction of throughput and/or a significant increase incomplexity. Accordingly, there exists a need for a low-complexity,high-throughput scheduling algorithm for MIMO broadcast channels withfinite rate feedback.

SUMMARY

The following presents a simplified summary of the claimed subjectmatter in order to provide a basic understanding of some aspects of theclaimed subject matter. This summary is not an extensive overview of theclaimed subject matter. It is intended to neither identify key orcritical elements of the claimed subject matter nor delineate the scopeof the claimed subject matter. Its sole purpose is to present someconcepts of the claimed subject matter in a simplified form as a preludeto the more detailed description that is presented later.

The following disclosure provides systems and methodologies formultiuser scheduling in a MIMO communication system. In accordance withvarious aspects described herein, full feedback scheduling is provided,wherein multiuser scheduling is accomplished via a MIMO broadcastscheduling algorithm that utilizes signal quality feedback from allusers. In one example, a base station in a wireless communication systemcan receive an antenna selection and signal quality feedback, such asSINR feedback, from each user in the system. Based on this information,the base station can transmit independent information streams fromrespective transmit antennas at the base station to respective userswith the highest signal quality. In another example, receive antennaselection can be employed to allow respective users to select a singlereceive antenna on which information is to be received from the basestation. Accordingly, optimal multiuser scheduling can be achieved for aMIMO communications system while requiring less complexity and powerthan traditional multiuser scheduling approaches, such as those thatemploy every receive antenna in the system as a virtual user.

Other aspects described herein provide quantized feedback scheduling,wherein signal quality feedback, such as SINR feedback, is quantizedinto a finite number of bits by respective users and sent back asfeedback to a base station. Such an approach can be used, for example,in a MIMO communications system with finite rate CSI feedback. In oneexample, the achievable sum-rate throughput of a MIMO communicationssystem increases as the number of users increases by using quantizedfeedback scheduling as described herein, even in cases where only 1 bitis allotted for quantized CSI feedback.

To the accomplishment of the foregoing and related ends, certainillustrative aspects of the claimed subject matter are described hereinin connection with the following description and the annexed drawings.These aspects are indicative, however, of but a few of the various waysin which the principles of the claimed subject matter can be employed.The claimed subject matter is intended to include all such aspects andtheir equivalents. Other advantages and novel features of the claimedsubject matter can become apparent from the following detaileddescription when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level block diagram of a wireless communication systemin accordance with various aspects.

FIG. 2 is a block diagram of a system for generating and providingfeedback to a base station in accordance with various aspects.

FIG. 3 is a block diagram of a component that schedules respective usersfor communication based on user feedback in accordance with variousaspects.

FIG. 4 is a block diagram of a system for transmitting information torespective users based on a communication schedule in accordance withvarious aspects.

FIG. 5 is a block diagram of a system for generating and providinglimited feedback to an access point in accordance with various aspects.

FIG. 6 is a block diagram of a component that schedules respective usersfor communication based on quantized user feedback in accordance withvarious aspects.

FIG. 7 is a flowchart of a method for multiuser scheduling in a wirelesscommunication system.

FIG. 8 is a flowchart of a method for scheduling users for communicationin a wireless communication system based on quantized user feedback.

FIG. 9 is a block diagram of an example operating environment in whichvarious aspects described herein can function.

FIG. 10 illustrates an overview of a wireless network environmentsuitable for service by various aspects described herein.

DETAILED DESCRIPTION

The claimed subject matter is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the claimed subject matter. It may beevident, however, that the claimed subject matter may be practicedwithout these specific details. In other instances, well-knownstructures and devices are shown in block diagram form in order tofacilitate describing the claimed subject matter.

As used in this application, the terms “component,” “system,” and thelike are intended to refer to a computer-related entity, eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component may be, but is not limited tobeing, a process running on a processor, a processor, an object, anexecutable, a thread of execution, a program, and/or a computer. By wayof illustration, both an application running on a server and the servercan be a component. One or more components may reside within a processand/or thread of execution and a component may be localized on onecomputer and/or distributed between two or more computers. Also, themethods and apparatus of the claimed subject matter, or certain aspectsor portions thereof, may take the form of program code (i.e.,instructions) embodied in tangible media, such as floppy diskettes,CD-ROMs, hard drives, or any other machine-readable storage medium,wherein, when the program code is loaded into and executed by a machine,such as a computer, the machine becomes an apparatus for practicing theclaimed subject matter. The components may communicate via local and/orremote processes such as in accordance with a signal having one or moredata packets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal).

Referring to FIG. 1, a high-level block diagram of a wirelesscommunication system 100 in accordance with various aspects presentedherein is illustrated. In accordance with one aspect, system 100 caninclude one or more base stations 110 that can communicate data, controlsignaling, and/or other information on a forward link or “downlink” toone or more receiving users 120 and 130. While not illustrated in FIG.1, it should be appreciated that one or more receiving users 120 and/or130 can additionally transmit information on a reverse link or “uplink”to a base station 110. Further, system 100 can include any number ofbase stations 110 and receiving users 120 and 130.

It should additionally be appreciated that receiving users 120 and/or130 can comprise and/or provide the functionality of a wirelessterminal, which can be connected to a computing device such as a laptopcomputer or desktop computer and/or a self-contained device such as acellular telephone, a personal digital assistant (PDA), or anothersuitable device. A wireless terminal can also be called a system,subscriber unit, subscriber station, mobile station, mobile, remotestation, remote terminal, access terminal, user terminal, user agent,user device, user equipment, etc. Additionally and/or alternatively, oneor more base stations 110 in the system 100 can comprise and/or providethe functionality of a wireless access point or Node B, for example,serving as a router between one or more other stations and a wirelessaccess network associated with the access point.

In accordance with one aspect, system 100 can comprise a single-cellwireless system, wherein base station 110 has M transmit (Tx) antennas112 that can communicate with K geographically dispersed users 120and/or 130, each having N receive (Rx) antennas 122 and/or 132. In oneexample, the number of users 120 and/or 130 can outnumber the number oftransmit antennas 112 at the base station 110, and the base station 110can have at least as many available transmit antennas 112 as the users120 and/or 130 have receive antennas. In another example, only J of theK users 120 and/or 130 are allowed to communicate with the base station110 simultaneously at any time slot t. In such an example, the set ofusers 120 and/or 130 which are assigned a non-zero rate of communicationwith the base station 110 at time slot t can be represented as A(t). Thecardinality of A(t) can be expressed as |A(t)|=J, with 1≦J≦K. If thek-th of the users 120 and/or 130 is an active user at time slot t, thenthe signal received by said user can be given by:

Y _(k™=)√{square root over (α_(k))}H _(k) ^(t) X ^(t) +W _(k) ^(t),kεA(t),  (1)

where X^(t) is an M×1 vector of the transmitted signals in time slot t,Y_(k) ^(t) is an N×1 vector of the received signal at the k-th user 120and/or 130 in time slot t, and W_(k) ^(t) is an N×1 vector of additivenoise whose entries are independent and identically distributed (i.i.d.)complex Gaussians with zero mean and variance N₀. As further used inEquation (1), H_(k) ^(t) is an N×M channel matrix constructed such thatthe (n,m)-th entry h_(k) ^(t)(n,m) CN (0,1) is an i.i.d. complexGaussian with zero mean and unit variance, where the scalar h_(k)^(t)(n,m) represents the complex channel gain from the m-th transmitantenna 112 at the base station 110 to the n-th receive antenna 122and/or 132 of the k-th user 120 and/or 130 at time slot t. The scalarα_(k) used in Equation (1) denotes the power attenuation coefficient onthe path from the base station 110 to the k-th user 120 and/or 130. Inone example, the attenuation coefficient α_(k) is equal to d_(k) ^(−c),where d_(k) denotes the distance from the base station 110 to the k-thuser 120 and c is a constant between 2 and 4.

In accordance with another aspect, the total transmitted power of thesystem 100 can be equal to 1, e.g., Tr(X^(t)[X^(t)]^(H))=1, where Tr(•)represents the trace of a matrix. Thus, the total transmitted power ofthe system 100 can be independent of the number of transmit antennas 112at the base station 110. In one example, the system 100 can utilize ablock-fading channel model, in which the channel h_(k) ^(t)(n,m)∀m,n,kis quasi-static and frequency non-selective in time slot t but variesindependently in different time slots. Additionally and/oralternatively, the channel h_(k) ^(t)(n,m) can be modeled asuncorrelated in time slot t with other channels h_(k′) ^(t)(n′,m′) forany (m′,n′,k′)≠(m,n,k). In accordance with one aspect, system 100 canoperate under an assumption that the MIMO channels are perfectly knownat the receiving users 120 and 130 but are unknown at the transmittingbase station 110.

In one example, a signal x(m) can be transmitted through an m-thtransmit antenna 112 at a given time slot. Further, for all transmitantennas m 112 where m=1, . . . , M, independent signals x(m) can betransmitted by the respective transmit antennas. In such an example, theaverage transmit power per transmit antenna 112 can be

$\frac{1}{M},$

e.g.,

${{E\left\lbrack {{x(m)}}^{2} \right\rbrack} = \frac{1}{M}},$

thereby ensuring that the combined transmit power of the transmitantennas 112 is 1. In this example, a signal received by the n-thantenna 122 and/or 132 of a k-th receiving user 120 and/or 130 from them-th transmit antenna 112 of the base station 110 can then be expressedas follows:

$\begin{matrix}{{y_{k}(n)} = {{\sqrt{\alpha_{k}}{\sum\limits_{m = 1}^{M}{{h_{k}\left( {n,m} \right)}{x(m)}}}} + {{w_{k}(n)}.}}} & (2)\end{matrix}$

By assuming that the signal x(m) is the desired signal for the k-threceiving user 120 and/or 130 and regarding other signals x(m′),m′≠m asinterference, the instantaneous SINR of the received signal y_(k)(n) canbe expressed as follows:

$\begin{matrix}{{{SINR}_{m,n}^{(k)}\frac{{{h_{k}\left( {n,m} \right)}}^{2}}{\frac{M}{{\overset{\_}{\gamma}}_{k}} + {\sum\limits_{m^{\prime} \neq m}{{h_{k}\left( {n,m^{\prime}} \right)}}^{2}}}},} & (3)\end{matrix}$

where γ _(k)=α_(k)/N_(O) is the downlink signal-to-noise ratio (SNR) ofthe k-th receiving user 120 and/or 130.

Existing approaches to downlink scheduling based on SINR feedback inMIMO networks generally regard each receive antenna 122 and/or 132 ateach receiver 120 and/or 130 as a separate virtual user. Thus, for aMIMO system having K users with N receive antennas each, such approacheseffectively treat the system as having NK single-antenna receivers.Based upon feedback of the maximum SINR of each of these virtual usersalong with the index m of a transmit antenna that achieves the maximumSINR for each virtual user, a base station can then transmit Mindependent signals to M virtual users with the highest SINRsimultaneously. The maximum throughput R achieved through such anapproach can be bounded by the following:

$\begin{matrix}{{R \leq {E\left\lbrack {\sum\limits_{m = 1}^{M}{\log_{2}\left( {1 + {\max\limits_{{n \in S_{r}},{k \in S_{u}}}{SINR}_{m,n}^{(k)}}} \right)}} \right\rbrack}},} & (4)\end{matrix}$

where S_(t)={1, . . . , M}, S_(r)={1, . . . , N} and S_(u)={1, . . . ,K}. This asymptotic analysis provided shows that the achievablethroughput of such an approach is M log log(NK) when K goes to infinity.However, these and/or other similar existing approaches require feedbackof KN SINR values, and the feedback load increases as the number ofreceive antennas increases.

In contrast to these existing approaches, the system 100 can include ascheduling component 114 that can facilitate MIMO downlink schedulingbased on feedback of only K real SINR values. In one example, thescheduling component 114 can facilitate downlink MIMO scheduling for asystem with finite rate feedback without inducing a loss of throughputtypically associated with existing limited feedback schedulingalgorithms. In accordance with one aspect, the scheduling component canschedule transmission of one or more signals at the transmit antennas112 to respective receiving users 120 and/or 130 based at least in partupon feedback received by the users 120 and/or 130. While the schedulingcomponent 114 is illustrated in FIG. 1 as located within the basestation 110, it should be appreciated that the scheduling component 114can be located anywhere within the communications system 100 that wouldenable the scheduling component 114 to be operatively associated withthe transmit antennas 112 and receive feedback from users 120 and/or130.

In accordance with one aspect, the scheduling component 114 can beoperable to implement a multiuser scheduling algorithm for MIMO downlinktransmission based on full SINR feedback from one or more users 120and/or 130. In one example, a full feedback scheduling algorithmimplementable by system 100 can be implemented as a two step process,wherein feedback is generated and communicated by users 120 and/or 130in a first step, followed by scheduling at the base station 110 in asecond step.

Feedback generation and transmission conducted in the first step of theabove algorithm is illustrated in the context of an example system 200in FIG. 2. As system 200 illustrates, users 220 in the system 200,represented by k(k=1, . . . , K), can firstly select their respectivemost favorable transmit antennas 222, represented byTx_(k)(Tx_(k)εS_(t)), and their respective most favorable receiveantennas 224, represented by Rx_(k)(Rx_(k)εS_(r)), for achieving amaximum SINR B_(k) 226. In one example, the maximum SINR 226 for a user220 can be expressed as follows:

$\begin{matrix}{B_{k} = {{\max\limits_{{m \in S_{t}},{n \in S_{r}}}{SINR}_{m,n}^{(k)}}..}} & (5)\end{matrix}$

Once a user 220 has generated parameters 222-226, the maximum SINR B_(k)226 and most favorable transmit antenna index Tx_(k) 222 can be sent bythe user 220 to a serving base station 210 for scheduling. While system200 illustrates generation and communication of parameters 222-226 at asingle user 220 for brevity, it should be appreciated that theoperations illustrated by FIG. 2 can be performed by any number of usersin an associated communication system.

Once transmit antenna indices and SINR values have been received at abase station from one or more users, FIG. 3 illustrates schedulingconducted in the second step of the above algorithm in the context of anexample scheduling component 300. It should be appreciated that theexample scheduling component 300 can be implemented, for example, at abase station (e.g., base station 110), at another appropriate networkentity, and/or as a stand-alone component in an associated communicationsystem. In one example, for each transmit antenna scheduled by thescheduling component 300, the scheduling component 300 can employ agrouping component 310 and/or another suitable component to clusterrespective users that provided the index of each transmit antenna intocorresponding groups 315. For example, the grouping component 310 cangroup users providing indices Tx_(k)=m, (m=1, . . . , M) into sets I_(m)such that:

I _(m) ={k|Tx _(k) =m,k=1, . . . ,K}.  (6)

Based on the generated groups 315, a selection component 320 and/oranother suitable component of the scheduling component 300 can thenselect the most favorable M users 325, which can be represented ask_(m)*(m=1, . . . , M), based on the following constraint:

$\begin{matrix}{{k_{m}^{*} = {\arg \; {\max\limits_{k \in I_{m}}B_{k}}}},{m = 1},\ldots \mspace{14mu},{M.}} & (7)\end{matrix}$

Once scheduling has been performed by selecting the most favorable usersk_(m)* for all m=1, . . . , M, transmission can be conducted based onthe determined schedule as illustrated by system 400 in FIG. 4. Assystem 400 illustrates, a scheduling component 412 at a base station 410can provide the determined most favorable users k_(m)* for respectivetransmit antennas 414. Based on this information, the transmit antennas412 can be used to communicate signals x(m) to respective selected users420 and/or 430. At the users 420 and/or 430, signals transmitted by thebase station 410 can then be received at most favorable receive antennasRx_(k*) _(m) 422 and/or 432. The most favorable receive antennas Rx_(k*)_(m) 422 and/or 432 for respective users 420 and/or 430 can bedetermined, for example, as described supra with regard to FIG. 2. Insuch an example, since receive antenna selection is performed at each ofthe users 420 and/or 430, the total feedback load at each time slot canbe regarded as K real values {B₁, . . . , B_(K)} plus K┌ log₂ M┐ bits ofTx_(k), ∀k , where ┌x┐ denotes the smallest integer larger than x.

In accordance with one aspect, the average achievable throughput of thefull feedback scheduling algorithm illustrated by FIGS. 2-4 overRayleigh fading channels can be derived as follows. Under Gaussian codesand minimum distance decoding at a receiver, the instantaneous sum-rateof scheduling with SINR feedback can be expressed by the following:

$\begin{matrix}{{R = {{\sum\limits_{m = 1}^{M}R_{m}} = {\sum\limits_{m = 1}^{M}{\log_{2}\left( {1 + {SINR}_{m,{Rx}_{k_{m}^{*}}}^{(k_{m}^{*})}} \right)}}}},} & (8)\end{matrix}$

where SINR_(m,Rxk*) _(m) ^((k*) ^(m) ⁾ is the SINR of a user k_(m)*scheduled by an m-th transmit antenna and Rx_(k*) _(m) is the receiveantenna selected by the user k_(m)*. Based on this expression, thefollowing can be observed:

$\begin{matrix}\begin{matrix}{{SINR}_{m,R_{k_{m}^{*}}}^{(k_{m}^{*})} = {\max\limits_{k \in I_{m}}B_{k}}} \\{{= {{\max\limits_{k \in I_{m}}{\max\limits_{{m \in S_{t}},{n \in S_{r}}}{SINR}_{m,n}^{(k)}}} \approx {\max\limits_{l \in {\{{1,\ldots \mspace{14mu},{NK}}\}}}Z_{l}}}},}\end{matrix} & (9) \\{Z_{l}\frac{{g_{l}}^{2}}{\frac{M}{{\overset{\_}{\gamma}}_{l}} + {\sum\limits_{j = 1}^{M - 1}{{\overset{\sim}{g}}_{l,j}}^{2}}}} & (10)\end{matrix}$

wherewith g_(l),{tilde over (g)}_(l,j) CN (0,1) for all l=1, . . . , NK andj=1, . . . , M−1. Since Prob{Tx_(k)=m}=1/M for any k and m for i.i.d.parameters h_(k)(n,m)∀n,m and k, it can be seen that E|I_(m)| approachesK|M. Thus, it can be assumed that the SINR of a scheduled user isselected from E|I_(m)|·|S_(t)|·|S_(r)|=NK NK candidates as shown in thelast step of Equation (9).

Further, it can be appreciated that since the parameters Z_(l) arei.i.d., Equation (10) can be rewritten as Z=X/(M/ γ+Y) for all identicalγ ₂, where X X²(2) and Y X² (2M−2). Thus, the probability densityfunction (PDF) and cumulative distribution function (CDF) of Z can begiven by the following:

$\begin{matrix}{{{f_{Z}(t)} = {\frac{^{{- {Mt}}/\overset{\_}{\gamma}}}{\left( {1 + t} \right)^{M}}\left( {{\frac{M}{\overset{\_}{\gamma}}\left( {1 + t} \right)} + M - 1} \right)}},{t \geq 0},} & (11) \\{{{F_{Z}(t)} = {1 - \frac{^{{- {Mt}}/\overset{\_}{\gamma}}}{\left( {1 + t} \right)^{M - 1}}}},{t \geq 0.}} & (12)\end{matrix}$

Next, the following denotations can be made:

$\begin{matrix}{{P\; {\max\limits_{k \in I_{m}}{\max\limits_{{m \in S_{t}},{n \in S_{r}}}{SINR}_{m,n}^{(k)}}}},} & (13) \\{U\; {\max\limits_{l \in {\{{1,\ldots \mspace{14mu},{NK}}\}}}{Z_{l}.}}} & (14)\end{matrix}$

Based on these denotations and by using Equations (8) and (9), systemthroughput pursuant to the scheduling algorithm illustrated by FIGS. 2-4averaged over Rayleigh fading channels can be given by the following:

$\begin{matrix}\begin{matrix}{{E(R)} = {\sum\limits_{m = 1}^{M}{E\; {\log_{2}\left( {1 + {SINR}_{m,{Rx}_{k_{m}^{*}}}^{(k_{m}^{*})}} \right)}}}} \\{{= {{M{\int_{0}^{\infty}{{\log_{2}\left( {1 + t} \right)}{f_{P}(t)}\ {t}}}} \approx {M{\int_{0}^{\infty}{{\log_{2}\left( {1 + t} \right)}{f_{U}(t)}\ {t}}}}}},}\end{matrix} & (15)\end{matrix}$

where f_(U)(t) is the PDF of U. Using order statistics, the followingcan be derived from Equation (14):

f _(U)(t)=NKf _(Z)(t)[F _(Z)(t)]^(NK-1)  (16)

for all identical γ _(l). Therefore, it can be observed that theachievable throughput of the MIMO downlink scheduling illustrated byFIGS. 2-4 averaged over fading channels can be expressed as follows:

E(R)≈KMN∫ ₀ ^(∞) log₂(1+t)f _(Z)(t)(F _(Z)(t))^(NK-1) dt.  (17)

It can be further appreciated that a numerical approximation for systemthroughput can be obtained by substituting Equations (11) and (12) intoEquation (17).

Under conventional scheduling approaches, each receive antenna is viewedas an individual user when N>1. By doing so, a K-user M×N system iseffectively converted to a K′-user M×N′ system, where K′=NK and N′=1. Inthe case where each user has only a single receive antenna, Equation(15) can also be used to evaluate the performance of these and/orsimilar conventional approaches for K′=NK and N′=1. By doing so, thefollowing can be derived:

$\begin{matrix}{{{E(R)} \approx {K^{\prime}{MN}{\int_{0}^{\infty}{{\log_{2}\left( {1 + t} \right)}{f_{Z}(t)}\left( {F_{Z}(t)} \right)^{{N^{\prime}K^{\prime}} - 1}\ {t}}}}} = {{NKM}{\int_{0}^{\infty}{{\log_{2}\left( {1 + t} \right)}{f_{Z}(t)}\left( {F_{Z}(t)} \right)^{{NK} - 1}\ {{t}.}}}}} & (18)\end{matrix}$

By observing the right hand side of Equation (17) compared to the righthand side of Equation (18), it can be observed that both equations areidentical. Thus, it can be appreciated that the scheduling algorithmillustrated by FIGS. 2-4 does not induce performance loss compared withconventional scheduling approaches.

In accordance with another aspect, the scheduling component 114 insystem 100 can utilize a scheduling algorithm based on quantized SINRfeedback from one or more users 120 and/or 130. In one example, aquantized feedback scheduling algorithm implementable by system 100 canbe implemented as a two step process, wherein quantized feedback isgenerated and communicated by users 120 and/or 130 in a first step,followed by scheduling at the base station 110 in a second step.

Generation and transmission of quantized feedback in the first step ofthe above quantized feedback algorithm is illustrated in the context ofan example system 500 in FIG. 5. As system 500 illustrates, userterminals 520 in the system 500, represented by k(k=1, . . . , K), canfirstly select respective indices 552 of most favorable transmitantennas Tx_(k)(Tx_(k)εS_(t)) and respective indices 524 of mostfavorable receive antennas Rx_(k)(Rx_(k)εS_(r)) for achieving a maximumSINR B_(k) 526. In one example, the maximum SINR 526 for a user 520 canbe expressed by Equation (5) supra. Following identification of areal-valued maximum SINR B_(k) 526, a user terminal can employ aquantization component 530 and/or another suitable component to quantizeB_(k) into one of a predetermined number of levels q_(k)=Q(B_(k)),thereby generating a quantized maximum SINR 532. In one example, thequantization levels utilized by the quantization component 530 can beexpressed as follows:

$\begin{matrix}{{Q\left( B_{k} \right)} = \left\{ {\begin{matrix}{0,} & {0 \leq B_{k} < \lambda_{1}} \\{i,} & {\lambda_{i} \leq B_{k} < \lambda_{i + 1}} \\{{L - 1},} & {B_{k} \geq \lambda_{L - 1}}\end{matrix},{i = 1},\ldots \mspace{14mu},{L - 2.}} \right.} & (19)\end{matrix}$

In one example, the number of levels L used for Q(B_(k)) can bedetermined at least in part by the number of bits b required torepresent a value B_(k), e.g., based on L=2^(b). Once a user 520 hasgenerated a Tx antenna index 522 and a quantized maximum SINR 532, theseand/or other parameters can be communicated to a base station or accesspoint 510 for scheduling. While system 500 illustrates generation andcommunication of feedback parameters at a single user 520 for brevity,it should be appreciated that the operations illustrated by FIG. 5 canbe performed by any number of users in an associated communicationsystem.

Once transmit antenna indices and quantized SINR values have beenreceived at a base station from one or more users, FIG. 6 illustratesexample scheduling that can be conducted by a scheduling component 600in the second step of the above quantized feedback algorithm. It shouldbe appreciated that the example scheduling component 600 can beimplemented, for example, at a base station (e.g., base station 110), atanother appropriate network entity, and/or as a stand-alone component inan associated communication system. In one example, for each transmitantenna scheduled by the scheduling component 600, the schedulingcomponent 600 can employ a grouping component 610 and/or anothersuitable component to cluster respective users providing the index ofeach transmit antenna into corresponding user sets 615. For example, thegrouping component 610 can group users providing index Tx_(k)=m into aset I_(m) as provided by Equation (6) above. From these generated usersets 615, a set refining component 620 and/or another suitable componentat the scheduling component 600 can further cluster users in user sets615 for each respective transmit antenna that achieve a maximum of q_(k)for all kεI_(m) into optimal user sets J_(m) 625. These optimal setsJ_(m) can be expressed as follows:

$\begin{matrix}{J_{m} = {\arg \; {\max\limits_{k \in I_{m}}{q_{k}.}}}} & (20)\end{matrix}$

From Equation (20), it can be appreciated that J_(m) ⊂I_(m) asJ_(m)={k|k=arg max(q_(k)),kεI_(m)}. From the optimal user sets 625, arandom selection component 630 and/or another suitable component canallocate an m-th transmit antenna to a user k_(m)* by randomly selectingusers from a set J_(m) as follows:

k _(m) *=rand{J _(m)}.  (21)

Once scheduling has been performed by selecting the most favorable usersk_(m)* for all m=1, . . . , M, transmission can be conducted based onthe determined schedule in a similar manner to that described above forfull feedback scheduling as illustrated by system 400 in FIG. 4.

In accordance with one aspect, the achievable throughput of thequantized feedback scheduling algorithm illustrated by FIGS. 5-6 can bederived as follows. Initially, it can be observed that the instantaneoussum-rate achieved by the quantized feedback scheduling techniqueillustrated by FIGS. 5-6 can be given by the following:

$\begin{matrix}\begin{matrix}{R = {\sum\limits_{m = 1}^{M}{\log_{2}\left( {1 + {SINR}_{m,{Rx}_{k_{m}^{*}}}^{(k_{m}^{*})}} \right)}}} \\{{= {\sum\limits_{m = 1}^{M}{\log_{2}\left( {1 + {{rand}\left\{ B_{k} \right\}_{k \in J_{m}}}} \right)}}},}\end{matrix} & (22)\end{matrix}$

where rand{B_(k)}_(kεJ) _(m) denotes the random selection of an elementfrom the set {B_(k)}_(kεJ) _(m) and B_(k) corresponds to the expressionfor SINR given by Equation (5). To simplify calculation,rand{B_(k)}_(kεJ) _(m) can be expressed simply as V. Accordingly, theaverage total throughput achieved by quantized feedback scheduling canbe given by the following:

E(R)=M∫ ₀ ^(∞) log₂(1+v)f _(V)(v)dv,  (23)

where f_(V)(v) denotes the PDF of V.

Next, if F_(V)(v) is used to denote the CDF of V and

${K^{\prime} = \frac{K}{M}},$

it can be appreciated that, for a large number of users, the cardinalityof the set I_(m) is equal to K′ for all m because

${{Prob}\left\{ {{Tx}_{k} = m} \right\}} = \frac{1}{M}$

for any k and m. By using A_(i) to denote the range [λ_(i),λ_(i+1)) fori=0, 1, . . . , L−1, where λ₀=0 and λ_(L)=∞, the CDF F_(V) can bederived for at least two cases as follows.

In one example, the first such case can hold when 0≦V<λ₁. In such acase, it can be appreciated that the maximum SINR B_(k) of a scheduleduser is in range A₀. Thus, it can be further appreciated that B_(k) forall kεI_(m) are also in range A₀. As result, the CDF of V can beexpressed as follows:

F _(V)(v)=F _(B)(v)[F _(B)(λ₁)]^(K′-1), 0≦v<λ ₁,  (24)

where F_(B)(v) denotes the CDF of B.

Additionally and/or alternatively, the second case can hold whenλ_(i)≦V<λ_(i+1) for i=1, . . . , L−1. In such a case, it can beappreciated that the maximum SINR B_(k) of a scheduled user is in rangeA_(i). Additionally, for any given 1≦r≦K′ in such a case, it can beassumed that there exist (r−1) other users having a B_(k) in regionA_(i). In accordance with one aspect, a scheduled user is selectedbecause it maximizes the value of B_(k) for a set of users. Accordingly,it can be assumed that the B_(k) of the remaining (K′−r) users must besmaller than λ_(i). In view of this observation, the CDF of V can beexpressed as follows:

$\begin{matrix}{{{F_{V}(v)} = {\left\lbrack {F_{B}\left( \lambda_{i} \right)} \right\rbrack^{K^{\prime}} + {\sum\limits_{r = 1}^{K^{\prime}}{{\begin{pmatrix}K^{\prime} \\r\end{pmatrix}\left\lbrack {{F_{B}(v)} - {F_{B}\left( \lambda_{i} \right)}} \right\rbrack} \times {\left\lbrack {F_{B}\left( \lambda_{i} \right)} \right\rbrack^{K^{\prime} - r}\left\lbrack {{F_{B}\left( \lambda_{i + 1} \right)} - {F_{B}\left( \lambda_{i} \right)}} \right\rbrack}^{r - 1}}}}},} & (25)\end{matrix}$

for λ_(i)≦v<λ_(i+1). Consequently, the corresponding PDF f_(V)(v) can begiven by the following:

$\begin{matrix}{{f_{V}(v)} = \left\{ \begin{matrix}{{{f_{B}(v)}\left\lbrack {F_{B}\left( \lambda_{1} \right)} \right\rbrack}^{K^{\prime} - 1},{{{for}\mspace{14mu} 0} \leq v < \lambda_{1}}} \\{{\sum\limits_{r = 1}^{K^{\prime}}{{\begin{pmatrix}K^{\prime} \\r\end{pmatrix}\left\lbrack {F_{B}\left( \lambda_{i} \right)} \right\rbrack}^{K^{\prime} - r} \times \left\lbrack {{F_{B}\left( \lambda_{i + 1} \right)} - {F_{B}\left( \lambda_{i} \right)}} \right\rbrack^{r - 1}{f_{B}(v)}}},} \\{{{{for}\mspace{14mu} \lambda_{i}} \leq v < \lambda_{i + 1}},{i = 1},\ldots \mspace{14mu},{L - 1},}\end{matrix} \right.} & (26)\end{matrix}$

where F_(B)(v)≈[F_(Z)(v)]^(MN) and f_(B)(v)≈MNf_(Z)(v)F_(Z)(v)^(MN-1).In one example, the PDF f_(Z)(v) and the CDF F_(Z)(v) are respectivelygiven by Equations (11) and (12). Further, by substituting Equation (26)into Equation (23), a numerical approximation for the achievablethroughput of quantized feedback scheduling as illustrated by FIGS. 5-6can be obtained.

By way of a specific, non-limiting example, quantized feedbackscheduling as illustrated by FIGS. 5-6 can be based on 1-bitquantization. In this 1-bit quantization example, one or more users canprovide a quantized value of 0 or 1 to a scheduling component accordingto a threshold λ₁, thereby obtaining a minimal feedback load. By way ofexample, for 1-bit quantization, Equation (26) can be rewritten asfollows:

$\begin{matrix}{{f_{V}(v)} = \left\{ \begin{matrix}{{\left\lbrack {F_{B}\left( \lambda_{1} \right)} \right\rbrack^{K^{\prime} - 1}{f_{B}(v)}},} & {0 \leq v < \lambda_{1}} \\{{\frac{1 - \left\lbrack {F_{B}\left( \lambda_{1} \right)} \right\rbrack^{K^{\prime}}}{1 - {F_{B}\left( \lambda_{1} \right)}}{f_{B}(v)}},} & {\lambda_{1} \leq {v.}}\end{matrix} \right.} & (27)\end{matrix}$

As a result, average achievable system throughput can be computed byapplying Equation (23) as follows:

$\begin{matrix}{{E(R)} = {{{M\left\lbrack {F_{B}\left( \lambda_{1} \right)} \right\rbrack}^{K^{\prime} - 1}{\int_{0}^{\lambda_{1}}{{\log_{2}\left( {1 + v} \right)}\ {f_{B}(v)}{v}}}} + {M\frac{1 - \left\lbrack {F_{B}\left( \lambda_{1} \right)} \right\rbrack^{K^{\prime}}}{1 - {F_{B}\left( \lambda_{1} \right)}}{\int_{\lambda_{1}}^{\infty}{{\log_{2}\left( {1 + v} \right)}{f_{B}(v)}\ {{v}.}}}}}} & (28)\end{matrix}$

In one example, Equation (28) can leverage approximation obtained fromF_(B)(v)≈[F_(Z)(v)]^(MN) and f_(B)(v)≈MNf_(Z)(v)F_(Z)(v)^(MN-1) withK′=K/M. Based on Equation (28), the PDF f_(Z)(v) and the CDF F_(Z)(v)are respectively given by Equations (11) and (12).

It can be observed from the above that as K goes to infinity (e.g.,while K′→∞), [F_(Z)(λ₁)]^(K′MN)→0 for a fixed value λ₁<∞. Accordingly,the throughput of Equation (28) can be derived as follows:

$\begin{matrix}{{\lim\limits_{K\rightarrow\infty}{E(R)}} \approx {\frac{M^{2}N}{1 - \left\lbrack {F_{Z}\left( \lambda_{1} \right)} \right\rbrack^{MN}} \times {\int_{\lambda_{1}}^{\infty}{{\log_{2}\left( {1 + v} \right)}{{f_{Z}(v)}\left\lbrack {F_{Z}(v)} \right\rbrack}^{{MN} - 1}\ {{v}.}}}}} & (29)\end{matrix}$

As shown by Equation (29), multiuser diversity can be lost for any fixedλ₁ as the total rate does not depend on the number of users K when Kgoes to infinity.

Further, it can be appreciated that when λ₁=0, the quantized feedbackscheduling illustrated by FIGS. 5-6 can be regarded as equivalent toRound-Robin scheduling, in which either a single user is scheduledrandomly from all users or multiple users are scheduled one by one in aloop. In one example, the throughput of such a case can be derived fromEquation (28) as follows:

E(R)≈M²N∫₀ ^(∞) log₂(1+v)f _(Z)(v)[F _(Z)(v)]^(MN-1) dv.  (30)

As Equation (30) demonstrates, multiuser diversity can be lost in thecase of λ₁=0 in a similar manner to Round-Robin scheduling.

Referring now to FIGS. 7-8, methodologies that can be implemented inaccordance with various aspects described herein are illustrated. While,for purposes of simplicity of explanation, the methodologies are shownand described as a series of blocks, it is to be understood andappreciated that the claimed subject matter is not limited by the orderof the blocks, as some blocks may, in accordance with the claimedsubject matter, occur in different orders and/or concurrently with otherblocks from that shown and described herein. Moreover, not allillustrated blocks may be required to implement the methodologies inaccordance with the claimed subject matter.

Furthermore, the claimed subject matter may be described in the generalcontext of computer-executable instructions, such as program modules,executed by one or more components. Generally, program modules includeroutines, programs, objects, data structures, etc., that performparticular tasks or implement particular abstract data types. Typicallythe functionality of the program modules may be combined or distributedas desired in various embodiments. Furthermore, as will be appreciatedvarious portions of the disclosed systems above and methods below mayinclude or consist of artificial intelligence or knowledge or rule basedcomponents, sub-components, processes, means, methodologies, ormechanisms (e.g., support vector machines, neural networks, expertsystems, Bayesian belief networks, fuzzy logic, data fusion engines,classifiers . . . ). Such components, inter alia, can automate certainmechanisms or processes performed thereby to make portions of thesystems and methods more adaptive as well as efficient and intelligent.

Referring to FIG. 7, a method 700 for multiuser scheduling in a wirelesscommunication system (e.g., system 100) is illustrated. At 702, transmitantenna indices (e.g., most favorable Tx antenna indices 222) andmaximum signal quality indicators (e.g., maximum SINR values 226)provided by respective user terminals (e.g., users 220) are identified(e.g., at a base station 210). At 704, the user terminals from whichtransmit antenna indices and maximum signal quality indicators areidentified at 702 are grouped (e.g., by a grouping component 310 at ascheduling component 300) into respective sets (e.g., sets 315) bytransmit antenna index. At 706, user terminals in respective setscreated at 704 having a highest indicated maximum signal quality amongthe user terminals in their respective sets (e.g., most favorable users325) are selected (e.g., by a selection component 320). At 708,information is transmitted to the user terminals selected at 706 via thetransmit antennas (e.g., Tx antennas 414) given as indices by the userterminals.

FIG. 8 illustrates a method 800 for scheduling users for communicationin a wireless communication system based on quantized user feedback. At802, transmit antenna indices (e.g., Tx antenna indices 522) andquantized signal quality indicators (e.g., quantized maximum SINR values532) provided by respective users (e.g., user terminals 520) areidentified (e.g., at an access point 510). At 804, the users from whichtransmit antenna indices and quantized signal quality indicators areidentified at 802 are grouped (e.g., by a grouping component 610 at ascheduling component 600) into respective sets (e.g., user sets 615) bytransmit antenna index. At 806, users are randomly selected (e.g., by arandom selection component 630) from among the users in respective setscreated at 804 that have a highest quantized signal quality indicator intheir respective sets (e.g., from among users provided in optimal userlists 625 provided by a set refining component 620). At 708, informationis transmitted to the users selected at 706 via the transmit antennasgiven as indices by the selected users.

Turning to FIG. 9, an exemplary non-limiting computing system oroperating environment in which various aspects described herein can beimplemented is illustrated. One of ordinary skill in the art canappreciate that handheld, portable and other computing devices andcomputing objects of all kinds are contemplated for use in connectionwith the claimed subject matter, e.g., anywhere that a communicationssystem may be desirably configured. Accordingly, the below generalpurpose remote computer described below in FIG. 9 is but one example ofa computing system in which the claimed subject matter can beimplemented.

Although not required, the claimed subject matter can partly beimplemented via an operating system, for use by a developer of servicesfor a device or object, and/or included within application software thatoperates in connection with one or more components of the claimedsubject matter. Software may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by one or more computers, such as client workstations, serversor other devices. Those skilled in the art will appreciate that theclaimed subject matter can also be practiced with other computer systemconfigurations and protocols.

FIG. 9 thus illustrates an example of a suitable computing systemenvironment 900 in which the claimed subject matter can be implemented,although as made clear above, the computing system environment 900 isonly one example of a suitable computing environment for a media deviceand is not intended to suggest any limitation as to the scope of use orfunctionality of the claimed subject matter. Further, the computingenvironment 900 is not intended to suggest any dependency or requirementrelating to the claimed subject matter and any one or combination ofcomponents illustrated in the example operating environment 900.

With reference to FIG. 9, an example of a remote device for implementingvarious aspects described herein includes a general purpose computingdevice in the form of a computer 910. Components of computer 910 caninclude, but are not limited to, a processing unit 920, a system memory930, and a system bus 921 that couples various system componentsincluding the system memory to the processing unit 920. The system bus921 can be any of several types of bus structures including a memory busor memory controller, a peripheral bus, and a local bus using any of avariety of bus architectures.

Computer 910 can include a variety of computer readable media. Computerreadable media can be any available media that can be accessed bycomputer 910. By way of example, and not limitation, computer readablemedia can comprise computer storage media and communication media.Computer storage media includes volatile and nonvolatile as well asremovable and non-removable media implemented in any method ortechnology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CDROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by computer 910. Communication media can embody computerreadable instructions, data structures, program modules or other data ina modulated data signal such as a carrier wave or other transportmechanism and can include any suitable information delivery media.

The system memory 930 can include computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) and/orrandom access memory (RAM). A basic input/output system (BIOS),containing the basic routines that help to transfer information betweenelements within computer 910, such as during start-up, can be stored inmemory 930. Memory 930 can also contain data and/or program modules thatare immediately accessible to and/or presently being operated on byprocessing unit 920. By way of non-limiting example, memory 930 can alsoinclude an operating system, application programs, other programmodules, and program data.

The computer 910 can also include other removable/non-removable,volatile/nonvolatile computer storage media. For example, computer 910can include a hard disk drive that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive thatreads from or writes to a removable, nonvolatile magnetic disk, and/oran optical disk drive that reads from or writes to a removable,nonvolatile optical disk, such as a CD-ROM or other optical media. Otherremovable/non-removable, volatile/nonvolatile computer storage mediathat can be used in the exemplary operating environment include, but arenot limited to, magnetic tape cassettes, flash memory cards, digitalversatile disks, digital video tape, solid state RAM, solid state ROMand the like. A hard disk drive can be connected to the system bus 921through a non-removable memory interface such as an interface, and amagnetic disk drive or optical disk drive can be connected to the systembus 921 by a removable memory interface, such as an interface.

A user can enter commands and information into the computer 910 throughinput devices such as a keyboard or a pointing device such as a mouse,trackball, touch pad, and/or other pointing device. Other input devicescan include a microphone, joystick, game pad, satellite dish, scanner,or the like. These and/or other input devices can be connected to theprocessing unit 920 through user input 940 and associated interface(s)that are coupled to the system bus 921, but can be connected by otherinterface and bus structures, such as a parallel port, game port or auniversal serial bus (USB). A graphics subsystem can also be connectedto the system bus 921. In addition, a monitor or other type of displaydevice can be connected to the system bus 921 via an interface, such asoutput interface 950, which can in turn communicate with video memory.In addition to a monitor, computers can also include other peripheraloutput devices, such as speakers and/or a printer, which can also beconnected through output interface 950.

The computer 910 can operate in a networked or distributed environmentusing logical connections to one or more other remote computers, such asremote computer 970, which can in turn have media capabilities differentfrom device 910. The remote computer 970 can be a personal computer, aserver, a router, a network PC, a peer device or other common networknode, and/or any other remote media consumption or transmission device,and can include any or all of the elements described above relative tothe computer 910. The logical connections depicted in FIG. 9 include anetwork 971, such local area network (LAN) or a wide area network (WAN),but can also include other networks/buses. Such networking environmentsare commonplace in homes, offices, enterprise-wide computer networks,intranets and the Internet.

When used in a LAN networking environment, the computer 910 is connectedto the LAN 971 through a network interface or adapter. When used in aWAN networking environment, the computer 910 can include acommunications component, such as a modem, or other means forestablishing communications over the WAN, such as the Internet. Acommunications component, such as a modem, which can be internal orexternal, can be connected to the system bus 921 via the user inputinterface at input 940 and/or other appropriate mechanism. In anetworked environment, program modules depicted relative to the computer910, or portions thereof, can be stored in a remote memory storagedevice. It should be appreciated that the network connections shown anddescribed are exemplary and other means of establishing a communicationslink between the computers can be used.

Turning now to FIG. 10, an overview of a network environment in whichthe claimed subject matter can be implemented is illustrated. Theabove-described systems and methodologies can be applied to any wirelesscommunication network; however, the following description sets forth anexemplary, non-limiting operating environment for said systems andmethodologies. The below-described operating environment should beconsidered non-exhaustive, and thus the below-described networkarchitecture is merely an example of a network architecture into whichthe claimed subject matter can be incorporated. It is to be appreciatedthat the claimed subject matter can be incorporated into any nowexisting or future alternative communication network architectures aswell.

Referring back to FIG. 10, various aspects of the global system formobile communication (GSM) are illustrated. GSM is one of the mostwidely utilized wireless access systems in today's fast growingcommunications systems. GSM provides circuit-switched data services tosubscribers, such as mobile telephone or computer users. General PacketRadio Service (“GPRS”), which is an extension to GSM technology,introduces packet switching to GSM networks. GPRS uses a packet-basedwireless communication technology to transfer high and low speed dataand signaling in an efficient manner. GPRS optimizes the use of networkand radio resources, thus enabling the cost effective and efficient useof GSM network resources for packet mode applications.

As one of ordinary skill in the art can appreciate, the exemplaryGSM/GPRS environment and services described herein can also be extendedto 3G services, such as Universal Mobile Telephone System (“UMTS”),Frequency Division Duplexing (“FDD”) and Time Division Duplexing(“TDD”), High Speed Packet Data Access (“HSPDA”), cdma2000 1x EvolutionData Optimized (“EVDO”), Code Division Multiple Access-2000 (“cdma20003x”), Time Division Synchronous Code Division Multiple Access(“TD-SCDMA”), Wideband Code Division Multiple Access (“WCDMA”), EnhancedData GSM Environment (“EDGE”), International MobileTelecommunications-2000 (“IMT-2000”), Digital Enhanced CordlessTelecommunications (“DECT”), etc., as well as to other network servicesthat shall become available in time. In this regard, the timingsynchronization techniques described herein may be applied independentlyof the method of data transport, and does not depend on any particularnetwork architecture or underlying protocols.

FIG. 10 depicts an overall block diagram of an exemplary packet-basedmobile cellular network environment, such as a GPRS network, in whichthe claimed subject matter can be practiced. Such an environment caninclude a plurality of Base Station Subsystems (BSS) 1000 (only one isshown), each of which can comprise a Base Station Controller (BSC) 1002serving one or more Base Transceiver Stations (BTS) such as BTS 1004.BTS 1004 can serve as an access point where mobile subscriber devices1050 become connected to the wireless network. In establishing aconnection between a mobile subscriber device 1050 and a BTS 1004, oneor more timing synchronization techniques as described supra can beutilized.

In one example, packet traffic originating from mobile subscriber 1050is transported over the air interface to a BTS 1004, and from the BTS1004 to the BSC 1002. Base station subsystems, such as BSS 1000, are apart of internal frame relay network 1010 that can include Service GPRSSupport Nodes (“SGSN”) such as SGSN 1012 and 1014. Each SGSN is in turnconnected to an internal packet network 1020 through which a SGSN 1012,1014, etc., can route data packets to and from a plurality of gatewayGPRS support nodes (GGSN) 1022, 1024, 1026, etc. As illustrated, SGSN1014 and GGSNs 1022, 1024, and 1026 are part of internal packet network1020. Gateway GPRS serving nodes 1022, 1024 and 1026 can provide aninterface to external Internet Protocol (“IP”) networks such as PublicLand Mobile Network (“PLMN”) 1045, corporate intranets 1040, orFixed-End System (“FES”) or the public Internet 1030. As illustrated,subscriber corporate network 1040 can be connected to GGSN 1022 viafirewall 1032; and PLMN 1045 can be connected to GGSN 1024 via boardergateway router 1034. The Remote Authentication Dial-In User Service(“RADIUS”) server 1042 may also be used for caller authentication when auser of a mobile subscriber device 1050 calls corporate network 1040.

Generally, there can be four different cell sizes in a GSMnetwork—macro, micro, pico, and umbrella cells. The coverage area ofeach cell is different in different environments. Macro cells can beregarded as cells where the base station antenna is installed in a mastor a building above average roof top level. Micro cells are cells whoseantenna height is under average roof top level; they are typically usedin urban areas. Pico cells are small cells having a diameter is a fewdozen meters; they are mainly used indoors. On the other hand, umbrellacells are used to cover shadowed regions of smaller cells and fill ingaps in coverage between those cells.

The claimed subject matter has been described herein by way of examples.For the avoidance of doubt, the subject matter disclosed herein is notlimited by such examples. In addition, any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs, nor is it meant to precludeequivalent exemplary structures and techniques known to those ofordinary skill in the art. Furthermore, to the extent that the terms“includes,” “has,” “contains,” and other similar words are used ineither the detailed description or the claims, for the avoidance ofdoubt, such terms are intended to be inclusive in a manner similar tothe term “comprising” as an open transition word without precluding anyadditional or other elements.

Additionally, the disclosed subject matter can be implemented as asystem, method, apparatus, or article of manufacture using standardprogramming and/or engineering techniques to produce software, firmware,hardware, or any combination thereof to control a computer or processorbased device to implement aspects detailed herein. The terms “article ofmanufacture,” “computer program product” or similar terms, where usedherein, are intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g., hard disk, floppy disk, magnetic strips . . . ), opticaldisks (e.g., compact disk (CD), digital versatile disk (DVD) . . . ),smart cards, and flash memory devices (e.g., card, stick). Additionally,it is known that a carrier wave can be employed to carrycomputer-readable electronic data such as those used in transmitting andreceiving electronic mail or in accessing a network such as the Internetor a local area network (LAN).

The aforementioned systems have been described with respect tointeraction between several components. It can be appreciated that suchsystems and components can include those components or specifiedsub-components, some of the specified components or sub-components,and/or additional components, according to various permutations andcombinations of the foregoing. Sub-components can also be implemented ascomponents communicatively coupled to other components rather thanincluded within parent components, e.g., according to a hierarchicalarrangement. Additionally, it should be noted that one or morecomponents can be combined into a single component providing aggregatefunctionality or divided into several separate sub-components, and anyone or more middle layers, such as a management layer, can be providedto communicatively couple to such sub-components in order to provideintegrated functionality. Any components described herein can alsointeract with one or more other components not specifically describedherein but generally known by those of skill in the art.

1. A system, comprising: a base station configured to communicate with aplurality of terminals via a multiple-input multiple-outputcommunication link; a plurality of transmit antennas at the base stationconfigured to transmit information to the plurality of terminals; and ascheduling component operatively associated with the base station, thescheduling component comprising: a grouping component configured togroup the plurality of terminals into sets corresponding to respectivetransmit antenna indices received from the plurality of terminals; and aselection component configured to identify a selected terminal, of theplurality of terminals, having a highest maximum signal qualityindication for a given set of the sets, and to assign a first transmitantenna, of the plurality of antennas, for transmission of informationto the selected terminal.
 2. The system of claim 1, wherein the firsttransmit antenna is configured to transmit the information to theselected terminal via a receive antenna selected by the selectedterminal from a plurality of available receive antennas.
 3. The systemof claim 1, wherein the selection component is configured to identifythe selected terminal based on respective maximum signal qualityindications received from the plurality of terminals
 4. The system ofclaim 3, wherein the scheduling component is configured to receive thetransmit antenna indices and the respective maximum signal qualityindications from the plurality of terminals over a feedback channel. 5.The system of claim 4, wherein the respective maximum signal qualityindications correspond to respective maximumsignal-to-interference-plus-noise (SINR) ratios corresponding to theplurality of terminals.
 6. The system of claim 4, wherein the respectivemaximum signal quality indications comprise respective quantized valuescorresponding to respective maximum signal qualities achievable at therespective plurality of terminals.
 7. The system of claim 6, wherein thequantized values are quantized into 2^(b) levels, where b is a number ofbits allotted for feedback of the respective maximum signal qualityindications.
 8. The system of claim 6, wherein at least one of thequantized values is a 1-bit value having a value of 1 in response to amaximum signal quality achievable by a corresponding terminal meeting afunction of a predetermined threshold, or a value of 0 in response tothe maximum signal quality achievable by the corresponding terminalfailing to meet the function of the predetermined value.
 9. The systemof claim 1, wherein the first transmit antenna corresponds to a transmitantenna index, of the plurality of transmit antenna indices,corresponding to the given set.
 10. A method, comprising: receivingtransmit antenna indices and signal quality indicators from a pluralityof terminals; clustering the plurality of terminals into at least twogroups respectively corresponding to at least two of the transmitantenna indices; selecting, from a first group of the at least twogroups, a terminal having a highest signal quality indicator within thefirst group; and scheduling a transmit antenna for transmission ofinformation to the terminal.
 11. The method of claim 10, furthercomprising transmitting information from the transmit antenna to theterminal via a receive antenna selected by the terminal.
 12. The methodof claim 10, wherein the receiving comprises receiving the transmitantenna indices and the signal quality indicators over a feedbackchannel.
 13. The method of claim 10, wherein the transmit antennacorresponds to a first transmit antenna index, of the at least twotransmit antenna indices, corresponding to the first group.
 14. Themethod of claim 10, wherein the receiving the signal quality indicatorsfrom the plurality of terminals comprises receiving indications ofmaximum signal-to-interference-plus-noise ratios (SINRs) respectivelyachievable by the plurality of terminals.
 15. The method of claim 10,wherein the receiving the signal quality indicators from the pluralityof terminals comprises receiving quantized values corresponding torespective maximum achievable signal qualities for the plurality ofterminals.
 16. The method of claim 15, wherein the receiving thequantized values includes receiving values quantized into 2^(b) levels,where b is a number of bits allotted for signal quality feedback. 17.The method of claim 16, wherein the clustering includes: clusteringreceiving terminals in the first group having quantized values thatindicate a highest quantized signal quality among the terminals in thefirst group yielding a custom group; and selecting a terminal from thecustom group in a substantially random manner.
 18. A system, comprising:means for identifying transmit antenna indices and maximum signalquality indicators provided by respective receivers; means for groupinga subset of the receivers providing a common transmit antenna index intoa set; means for selecting a receiver in the set having a highestmaximum signal quality indicator; and means for transmitting informationto the receiver via a transmit antenna corresponding to the commontransmit antenna index.
 19. The system of claim 18, wherein the meansfor identifying comprises means for identifying quantized signal qualityindicators, and the means for selecting comprises means for randomlyselecting receivers in the set having a highest quantized signal qualityindicator.
 20. A computer-readable storage device having stored thereoncomputer-executable instructions that, in response to execution, cause acomputing system to perform operations, comprising: receiving transmitantenna indices and signal quality indicators from respective terminals;grouping a subset of the terminals having a same transmit antenna indexinto a set; selecting, from the set, a terminal having a highest signalquality indicator; and scheduling a transmit antenna corresponding tothe same transmit antenna index for transmission of information to theterminal.